Bibliographic record
Abstract
This study is based on field survey conducted in a Mossi village located about 110km northwest of the capital city of Burkina Faso, Ouagadougou. Out of 90 household heads in the village, 32 were interviewed. All were peasant farmers who produce millet, sorghum, maize and minor crops, such as yams, sweet potatoes and groundnuts. Even in a good harvest season, about a quarter of households can not meet their consumption needs. In a bad harvest, more than 80% of households experience grain deficits. Of the 131 married male members of these 32 households, 71 or 54% are migrants living in the Ivory Coast. About 90% of these migrants are farmers. Two-thirds of the farmers have their own farm land on which they cultivate cocoa and/or coffee, and the rest are farm labourers. Many of these migrants send home remittances, which constitute indispensable income for the villagers, not only for covering deficiencies in food supply but also for agricultural investment, such as the purchase of fertilizers. Until the end of the 1960s, people migrated to the Ivory Coast as forced labour or as a consequence of the government's labour recruitment policy. Since then, however, circumstances have changed. People have migrated in search of more stable and prosperous conditions for engaging in agricultural production, while abandoning unsustainable production under precarious weather conditions back in Burkina Faso. They have purchased land and started to cultivate commercial crops. But this effort to strengthen their access to land and more sustainable agricultural income has exposed them to unexpected risks. The abrupt emergence of an anti-Burkinabe (Burkina people) movement since last year's presidential election has shown that the vulnerability of landholding migrants in the Ivory Coast is more serious than that of farm labourers. The vulnerability of the Mossi village as a consequence also increased.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".